Theoretical analysis of evolutionary algorithms with an infinite population size in continuous space. Part II: Analysis of the diversification role of crossover

1994 ◽  
Vol 5 (1) ◽  
pp. 120-129 ◽  
Author(s):  
Xiaofeng Qi ◽  
F. Palmieri
Keyword(s):  
Theoretical Analysis ◽  
Evolutionary Algorithms ◽  
Population Size ◽  
Continuous Space ◽  
Infinite Population

scholarly journals A Revisit of Infinite Population Models for Evolutionary Algorithms on Continuous Optimization Problems

2020 ◽  
Vol 28 (1) ◽  
pp. 55-85
Author(s):  
Bo Song ◽  
Victor O.K. Li
Keyword(s):  
Population Dynamics ◽  
Evolutionary Algorithms ◽  
Population Size ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Analytical Framework ◽  
Population Models ◽  
Initial Population ◽  
Infinite Population ◽  
Continuous Optimization Problems

Infinite population models are important tools for studying population dynamics of evolutionary algorithms. They describe how the distributions of populations change between consecutive generations. In general, infinite population models are derived from Markov chains by exploiting symmetries between individuals in the population and analyzing the limit as the population size goes to infinity. In this article, we study the theoretical foundations of infinite population models of evolutionary algorithms on continuous optimization problems. First, we show that the convergence proofs in a widely cited study were in fact problematic and incomplete. We further show that the modeling assumption of exchangeability of individuals cannot yield the transition equation. Then, in order to analyze infinite population models, we build an analytical framework based on convergence in distribution of random elements which take values in the metric space of infinite sequences. The framework is concise and mathematically rigorous. It also provides an infrastructure for studying the convergence of the stacking of operators and of iterating the algorithm which previous studies failed to address. Finally, we use the framework to prove the convergence of infinite population models for the mutation operator and the [Formula: see text]-ary recombination operator. We show that these operators can provide accurate predictions for real population dynamics as the population size goes to infinity, provided that the initial population is identically and independently distributed.

scholarly journals On the Choice of the Offspring Population Size in Evolutionary Algorithms

2005 ◽  
Vol 13 (4) ◽  
pp. 413-440 ◽  
Author(s):  
Thomas Jansen ◽  
Kenneth A. De Jong ◽  
Ingo Wegener
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Population Size ◽  
Diffi Cult ◽  
Very High

Evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a diffi- cult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified guidelines to help the practitioner find good values for these parameters. One such parameter is the offspring population size. Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. The result is a much better understanding of the role of offspring population size in an EA and suggests a simple way to dynamically adapt this parameter when necessary.

The role of S and Mo doping on the dissociation of water molecule on FeOCl surface: Experimental and theoretical analysis

2021 ◽  
pp. 131353
Author(s):  
Xiao Liu ◽  
Wenyi Zhang ◽  
Mingguo Peng ◽  
Guangqun Zhai ◽  
Linchao Hu ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Water Molecule ◽  
Mo Doping

scholarly journals Population Density or Populations Size. Which Factor Determines Urban Traffic Congestion?

2021 ◽  
Vol 13 (8) ◽  
pp. 4280
Author(s):  
Yu Sang Chang ◽  
Sung Jun Jo ◽  
Yoo-Taek Lee ◽  
Yoonji Lee
Keyword(s):  
Population Density ◽  
Population Size ◽  
Traffic Congestion ◽  
Critical Role ◽  
Small Population ◽  
High Density ◽  
Urban Traffic ◽  
Contradictory Result ◽  
Low Density

A large number of articles have documented that as population density of cities increases, car use declines and public transit use rises. These articles had a significant impact of promoting high-density compact urban development to mitigate traffic congestion. Another approach followed by other researchers used the urban scaling model to indicate that traffic congestion increases as population size of cities increases, thus generating a possible contradictory result. Therefore, this study examines the role of both density and population size on traffic congestion in 164 global cities by the use of Stochastic Impacts by Regression on Population, Affluence and Technology model. We divide 164 cities into the two subgroups of 66 low density cities and 98 high density cities for analysis. The findings from the subgroups analysis indicated a clear-cut difference on the critical role of density in low-density cities and the exclusive role of population size in high-density cities. Furthermore, using threshold regression model, 164 cities are divided into the two regions of large and small population cities to determine population scale advantage of traffic congestion. Our findings highlight the importance of including analysis of subgroups based on density and/or population size in future studies of traffic congestion.

Evaluation-driven design of a robust coreference resolution system

2003 ◽  
Vol 9 (3) ◽  
pp. 281-306 ◽  
Author(s):  
ANDREI POPESCU-BELIS
Keyword(s):  
Working Memory ◽  
Theoretical Analysis ◽  
Coreference Resolution ◽  
Knowledge Sources ◽  
Narrative Texts ◽  
Data Annotation ◽  
Evaluation Measures ◽  
Optimal Behavior ◽  
Resolution System

In this paper, we describe a system for coreference resolution and emphasize the role of evaluation for its design. The goal of the system is to group referring expressions (identified beforehand in narrative texts) into sets of coreferring expressions that correspond to discourse entities. Several knowledge sources are distinguished, such as referential compatibility between a referring expression and a discourse entity, activation factors for discourse entities, size of working memory, or meta-rules for the creation of discourse entities. For each of them, the theoretical analysis of its relevance is compared to scores obtained through evaluation. After looping through all knowledge sources, an optimal behavior is chosen, then evaluated on test data. The paper also discusses evaluation measures as well as data annotation, and compares the present approach to others in the field.

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